class_docstr: Implementation of `PISA SSD head <https://arxiv.org/abs/1904.04821>`_
  
  Args:
      num_classes (int): Number of categories excluding the background
          category.
      in_channels (Sequence[int]): Number of channels in the input feature
          map.
      stacked_convs (int): Number of conv layers in cls and reg tower.
          Defaults to 0.
      feat_channels (int): Number of hidden channels when stacked_convs
          > 0. Defaults to 256.
      use_depthwise (bool): Whether to use DepthwiseSeparableConv.
          Defaults to False.
      conv_cfg (:obj:`ConfigDict` or dict, Optional): Dictionary to construct
          and config conv layer. Defaults to None.
      norm_cfg (:obj:`ConfigDict` or dict, Optional): Dictionary to construct
          and config norm layer. Defaults to None.
      act_cfg (:obj:`ConfigDict` or dict, Optional): Dictionary to construct
          and config activation layer. Defaults to None.
      anchor_generator (:obj:`ConfigDict` or dict): Config dict for anchor
          generator.
      bbox_coder (:obj:`ConfigDict` or dict): Config of bounding box coder.
      reg_decoded_bbox (bool): If true, the regression loss would be
          applied directly on decoded bounding boxes, converting both
          the predicted boxes and regression targets to absolute
          coordinates format. Defaults to False. It should be `True` when
          using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
      train_cfg (:obj:`ConfigDict` or dict, Optional): Training config of
          anchor head.
      test_cfg (:obj:`ConfigDict` or dict, Optional): Testing config of
          anchor head.
      init_cfg (:obj:`ConfigDict` or dict or list[:obj:`ConfigDict` or             dict], Optional): Initialization config dict.
docstr: Compute losses of the head.
  
  Args:
      cls_scores (list[Tensor]): Box scores for each scale level
          Has shape (N, num_anchors * num_classes, H, W)
      bbox_preds (list[Tensor]): Box energies / deltas for each scale
          level with shape (N, num_anchors * 4, H, W)
      batch_gt_instances (list[:obj:`InstanceData`]): Batch of
          gt_instance.  It usually includes ``bboxes`` and ``labels``
          attributes.
      batch_img_metas (list[dict]): Meta information of each image, e.g.,
          image size, scaling factor, etc.
      batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
          Batch of gt_instances_ignore. It includes ``bboxes`` attribute
          data that is ignored during training and testing.
          Defaults to None.
  
  Returns:
      dict[str, Union[List[Tensor], Tensor]]: A dictionary of loss
      components. the dict has components below:
  
      - loss_cls (list[Tensor]): A list containing each feature map             classification loss.
      - loss_bbox (list[Tensor]): A list containing each feature map             regression loss.
      - loss_carl (Tensor): The loss of CARL.
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code: def loss_by_feat(
      self,
      cls_scores: List[Tensor],
      bbox_preds: List[Tensor],
      batch_gt_instances: InstanceList,
      batch_img_metas: List[dict],
      batch_gt_instances_ignore: OptInstanceList = None,
  ) -> Dict[str, Union[List[Tensor], Tensor]]:
      """Compute losses of the head.
  
          Args:
              cls_scores (list[Tensor]): Box scores for each scale level
                  Has shape (N, num_anchors * num_classes, H, W)
              bbox_preds (list[Tensor]): Box energies / deltas for each scale
                  level with shape (N, num_anchors * 4, H, W)
              batch_gt_instances (list[:obj:`InstanceData`]): Batch of
                  gt_instance.  It usually includes ``bboxes`` and ``labels``
                  attributes.
              batch_img_metas (list[dict]): Meta information of each image, e.g.,
                  image size, scaling factor, etc.
              batch_gt_instances_ignore (list[:obj:`InstanceData`], Optional):
                  Batch of gt_instances_ignore. It includes ``bboxes`` attribute
                  data that is ignored during training and testing.
                  Defaults to None.
  
          Returns:
              dict[str, Union[List[Tensor], Tensor]]: A dictionary of loss
              components. the dict has components below:
  
              - loss_cls (list[Tensor]): A list containing each feature map \
              classification loss.
              - loss_bbox (list[Tensor]): A list containing each feature map \
              regression loss.
              - loss_carl (Tensor): The loss of CARL.
          """
      featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
      assert len(featmap_sizes) == self.prior_generator.num_levels
  
      device = cls_scores[0].device
  
      anchor_list, valid_flag_list = self.get_anchors(
          featmap_sizes, batch_img_metas, device=device
      )
      cls_reg_targets = self.get_targets(
          anchor_list,
          valid_flag_list,
          batch_gt_instances,
          batch_img_metas,
          batch_gt_instances_ignore=batch_gt_instances_ignore,
          unmap_outputs=False,
          return_sampling_results=True,
      )
      (
          labels_list,
          label_weights_list,
          bbox_targets_list,
          bbox_weights_list,
          avg_factor,
          sampling_results_list,
      ) = cls_reg_targets
  
      num_images = len(batch_img_metas)
      all_cls_scores = torch.cat(
          [
              s.permute(0, 2, 3, 1).reshape(
                  num_images, -1, self.cls_out_channels
              )
              for s in cls_scores
          ],
          1,
      )
      all_labels = torch.cat(labels_list, -1).view(num_images, -1)
      all_label_weights = torch.cat(label_weights_list, -1).view(
          num_images, -1
      )
      all_bbox_preds = torch.cat(
          [
              b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
              for b in bbox_preds
          ],
          -2,
      )
      all_bbox_targets = torch.cat(bbox_targets_list, -2).view(
          num_images, -1, 4
      )
      all_bbox_weights = torch.cat(bbox_weights_list, -2).view(
          num_images, -1, 4
      )
  
      # concat all level anchors to a single tensor
      all_anchors = []
      for i in range(num_images):
          all_anchors.append(torch.cat(anchor_list[i]))
  
      isr_cfg = self.train_cfg.get("isr", None)
      all_targets = (
          all_labels.view(-1),
          all_label_weights.view(-1),
          all_bbox_targets.view(-1, 4),
          all_bbox_weights.view(-1, 4),
      )
      # apply ISR-P
      if isr_cfg is not None:
          all_targets = isr_p(
              all_cls_scores.view(-1, all_cls_scores.size(-1)),
              all_bbox_preds.view(-1, 4),
              all_targets,
              torch.cat(all_anchors),
              sampling_results_list,
              loss_cls=CrossEntropyLoss(),
              bbox_coder=self.bbox_coder,
              **self.train_cfg["isr"],
              num_class=self.num_classes,
          )
          (
              new_labels,
              new_label_weights,
              new_bbox_targets,
              new_bbox_weights,
          ) = all_targets
          all_labels = new_labels.view(all_labels.shape)
          all_label_weights = new_label_weights.view(
              all_label_weights.shape
          )
          all_bbox_targets = new_bbox_targets.view(
              all_bbox_targets.shape
          )
          all_bbox_weights = new_bbox_weights.view(
              all_bbox_weights.shape
          )
  
      # add CARL loss
      carl_loss_cfg = self.train_cfg.get("carl", None)
      if carl_loss_cfg is not None:
          loss_carl = carl_loss(
              all_cls_scores.view(-1, all_cls_scores.size(-1)),
              all_targets[0],
              all_bbox_preds.view(-1, 4),
              all_targets[2],
              SmoothL1Loss(beta=1.0),
              **self.train_cfg["carl"],
              avg_factor=avg_factor,
              num_class=self.num_classes,
          )
  
      # check NaN and Inf
      assert (
          torch.isfinite(all_cls_scores).all().item()
      ), "classification scores become infinite or NaN!"
      assert (
          torch.isfinite(all_bbox_preds).all().item()
      ), "bbox predications become infinite or NaN!"
  
      losses_cls, losses_bbox = multi_apply(
          self.loss_by_feat_single,
          all_cls_scores,
          all_bbox_preds,
          all_anchors,
          all_labels,
          all_label_weights,
          all_bbox_targets,
          all_bbox_weights,
          avg_factor=avg_factor,
      )
      loss_dict = dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
      if carl_loss_cfg is not None:
          loss_dict.update(loss_carl)
      return loss_dict
  
file_path: mmdetection/mmdet/models/dense_heads/pisa_ssd_head.py
imports: from typing import Dict, List, Union
  import torch
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.utils import InstanceList, OptInstanceList
  from ..losses import CrossEntropyLoss, SmoothL1Loss, carl_loss, isr_p
  from ..utils import multi_apply
  from .ssd_head import SSDHead
label_desc: Use image node_id 1 for calling the PISASSDHead obj's underlying member method code with example usage: obj.loss_by_feat(cls_scores, bbox_preds, batch_gt_instances, batch_img_metas, batch_gt_instances_ignore) and returns: loss_dict